Characterizing Long-Running Political Phenomena on Social Media Emre Calisir Marco Brambilla Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano, Milan, Italy Email:{firstname.lastname}@polimi.it Abstract Social media provides many opportunities to monitor and evaluate political phenomena such as referendums and elections. In this study, we propose a set of approaches to analyze long-running political events on social media with a real-world experiment: the debate about Brexit, i.e., the process through which the United Kingdom activated the option of leaving the European Union. We address the following research questions: Could Twitter-based stance classification be used to demonstrate public stance with respect to political events? What is the most efficient and comprehensive approach to measuring the impact of politicians on social media? Which of the polarized sides of the debate is more responsive to politician messages and the main issues of the Brexit process? What is the share of bot accounts in the Brexit discussion and which side are they for? By combining the user stance classification, topic discovery, sentiment analysis, and bot detection, we show that it is possible to obtain useful insights about political phenomena from social media data. We are able to detect relevant topics in the discussions, such as the demand for a new referendum, and to understand the position of social media users with respect to the different topics in the debate. Our comparative and temporal analysis of political accounts can detect the critical periods of the Brexit process and the impact they have on the debate. Keywords: Brexit, referendum, elections, topic discovery, stance classification, political social media bots 1 Introduction long-standing discussion well beyond the end of referendum, as seen in the continuity of the Google search trend (Fig.1). Social media provides many opportunities to monitor and eval- Another indicator of the constant interest in the subject is uate political phenomena such as referendums and elections. the continuing discussion on Twitter, whose trend is highly Citizens from all around the world, voters, politicians, private correlated to the Google search trend on the topic (Pearson and public authorities participate and contribute to debates correlation of 0.92). This result makes Twitter a convenient on social media platforms with tremendous interest. Accord- data source to analyze the Brexit phenomenon with respect to ing to a survey, 66% of social media users have employed these various aspects. platforms to post their thoughts about civic and political is- In this study, we aim to address the following research ques- sues, react to others’ postings, press friends to act on issues arXiv:1901.00740v1 [cs.CY] 23 Dec 2018 tions: and vote, follow candidates, like and link to others’ content, Question 1: Can we determine the political stance of Twit- and belong to groups formed on social networking sites [1]. In ter users with respect to Brexit based on the content they this context, Twitter is known as one of the most convenient share? Can we analyze how stance evolves in time? social media platforms with its prominent features including Question 2: What are the main discussion topics, what is hashtag based information annotation and retrieval, mention- the general sensitivity on these issues and which polarized side based people referring and re-tweet/like based agreement on reacts to the different issues? the opinions. Segesten and Bossetta found that that citizens - not political parties - are the primary initiators and sharers of Question 3: Which politicians have been discussed most, political calls for action leading up to the 2015 British General what is the general sensitivity with respect to these politicians Elections [2]. and which polarized side is more responsive to them? The political issue investigated in this study concerns one of Question 4: What is the impact of automated bot accounts the most important political events of recent times, which de- to the online discussions, and to which side are they aligned fines the process of United Kingdom’s exit from the European most? Union (EU), informally named Brexit. On 23 June 2016, the The rest of the paper is organized as follows: We first present United Kingdom voted to leave the EU, by 51.9% for Leave, the primary studies in our research focus. Then we give de- and 48.1% for Remain side. However, the local and global im- tailed information about our collected data and findings in- pacts of the referendum have made the issue a highly active and cluding user demographics and public interest to tweets. We 1 Volume a government policy, a movement, a product, and so on. On # of Google Searches # of Tweets (log-scale) the other hand, stance classification is usually confused with 1000000 sentiment detection. According to [7], while in sentiment anal- 100000 ysis the goal is to extract the sentiment from a piece of text, 10000 in stance classification the purpose is to determine favorability 1000 toward a given (pre-chosen) target of interest. The examples in 100 Table 1 show the difference; tweets may have the same stance, 10 but opposite sentiment. Date 1 Tweets have by nature a concise text structure, which makes the stance classification task more challenging. To overcome this obstacle, many studies have focused on the different steps of machine learning pipeline. For the data annotation part Figure 1: After the referendum, public interest to Brexit of the supervised learning task, manual [8, 9, 10] or automat- debate continues with the same trend in terms of Twitter ical [11] methods have been used. Besides, there also exist posts and Google searches (Correlation 0.92) some specific studies presenting richer datasets in order to de- fine a gold standard [12]. Specifically for Twitter, various fea- ture engineering techniques are implemented such as lexical (n- present our two-fold stance classification approach and experi- grams), word-embedding [7], syntactic (sentiment, grammat- ment on the Brexit referendum. Next, we analyze the Twitter ical) [13, 3], meta-data (retweet count, follower count, men- accounts regarding their bot behavior, and then we interpret tions), network-specific (retweet-based propagation)[14] and the stance of bot accounts for the Brexit experiment. In topic argumentative analysis(argumentativeness, source type) [8]. analysis part, we share the results of topic discovery implemen- As a machine learning algorithm, the authors achieved success- tation concerning the public attitude and sentiment of discov- ful results with Naive Bayes[8, 14], Support Vector Machines ered topics. Additionally, we share our findings of engagement (SVM) [7, 8, 9], Decision Trees [8] and Recurrent Neural Net- of social media users with the politicians, and we analyze the work (RNN) [15], and a combination of RNN with long-short public reaction to these accounts over time. We conclude our memory (LSTM) and target-specific attention extractor [16]. work by providing the technical details of our implementation, As a complementary step of stance classification, some stud- and with the detailed tables in the appendix section. ies also have applied an age-adjustment since Twitter users do not represent the demographics of voters genuinely. In a recent 2 Related Work study, Grcar et al. argue that the age correction changed their prediction outcome from Remain to Leave, by achieving a very 2.1 Social Media and Politics close ratio compared to referendum outcome [9]. In another study, Lopez et al. achieved 71% correlation for Leave and Social media has an essential role in terms of sharing informa- 65% for Remain without applying any age adjustment [11]. tion during political happenings. In a study related to German federal elections, the authors found that Twitter is used inten- sively for political deliberation [3]. In another study related to 2013 Italian elections, Vaccari et al. demonstrated that the 2.3 Role of Automated Accounts (Bots) in political deliberation on social media also makes people more Elections and Referendums conscious and active on the political news [4]. In a recent study on the Brexit referendum, it is argued that social media data While social media is a platform made for the use of people, could be used to elucidate the underlying themes/concerns of it is also known that a large share of accounts are automated the political discourse [5]. The intense use of social media in generators of posts and other activities on social networks. politics makes this platform a vast source of information for These accounts are often referred to as bots. A type of bots is understanding various aspects of human behavior and political political social media bots specializing in political issues that facts. are particularly active in public policies, elections, and po- larized political discussions [17]. However, their presence in 2.2 Stance Classification online political discussions could be harmful in many senses. Ratkiewicz et al.’s work on 2010 US Midterm elections and In recent years, the researchers have shown great interest to Metaxas et al.’s work on 2010 Massachusetts special election estimate public opinions about political phenomenon through showed that political bots might artificially inflate support for social media data. Even though there exist some studies [6] ar- a political candidate [6, 18]. In a recent study on 2016 US guing that social media could not be used as a source for elec- Elections, Bessi and Ferrara found that bot accounts gener- toral predictions in general, several studies achieved notable ated about one-fifth of the entire conversation, and their pres- results. Identifying the users who are in favor of, against, or ence negatively affected democratic political discussion rather neutral towards a target is known as stance classification. The than improving it, which in turn could potentially alter public target of the stance analysis may be a person, an organization, opinion and endanger the integrity of the Presidential elections 2 Table 1: Stance and sentiment analysis are separate tasks, expressions from a specific stance may have opposite sentiment.